UchenML Lets Developers Compile Neural Networks Directly Into C++ Binaries

UchenML is a C++20 machine learning framework that embeds trained neural network models as compile-time constants directly inside C++ binaries, eliminating the need for separate model loaders or runtime file parsing. The framework represents model architecture as composable layer values, allowing parameter counts and shapes to be verified at compile time via static assertions. Weights are stored as flat float arrays and can be memory-mapped or packed in fp16 format, as demonstrated in a live browser demo running a model with over 2.7 million parameters. Training is supported within the same framework using immutable state updates and optimizers such as SGD and Adam, with the final output being a plain float array ready for deployment. Built with Bazel and tested across MSVC, GCC, and Clang, UchenML prioritizes portability and integration ease over Python-based alternatives.